simstudy (version 0.7.0)

addCorGen: Create multivariate (correlated) data - for general distributions

Description

Create multivariate (correlated) data - for general distributions

Usage

addCorGen(
  dtOld,
  nvars = NULL,
  idvar = "id",
  rho = NULL,
  corstr = NULL,
  corMatrix = NULL,
  dist,
  param1,
  param2 = NULL,
  cnames = NULL,
  method = "copula",
  ...
)

Value

Original data.table with added column(s) of correlated data

Arguments

dtOld

The data set that will be augmented. If the data set includes a single record per id, the new data table will be created as a "wide" data set. If the original data set includes multiple records per id, the new data set will be in "long" format.

nvars

The number of new variables to create for each id. This is only applicable when the data are generated from a data set that includes one record per id.

idvar

String variable name of column represents individual level id for correlated data.

rho

Correlation coefficient, -1 <= rho <= 1. Use if corMatrix is not provided.

corstr

Correlation structure of the variance-covariance matrix defined by sigma and rho. Options include "cs" for a compound symmetry structure and "ar1" for an autoregressive structure.

corMatrix

Correlation matrix can be entered directly. It must be symmetrical and positive semi-definite. It is not a required field; if a matrix is not provided, then a structure and correlation coefficient rho must be specified.

dist

A string indicating "normal", "binary", "poisson" or "gamma".

param1

A string that represents the column in dtOld that contains the parameter for the mean of the distribution. In the case of the uniform distribution the column specifies the minimum.

param2

A string that represents the column in dtOld that contains a possible second parameter for the distribution. For the normal distribution, this will be the variance; for the gamma distribution, this will be the dispersion; and for the uniform distribution, this will be the maximum.

cnames

Explicit column names. A single string with names separated by commas. If no string is provided, the default names will be V#, where # represents the column.

method

Two methods are available to generate correlated data. (1) "copula" uses the multivariate Gaussian copula method that is applied to all other distributions; this applies to all available distributions. (2) "ep" uses an algorithm developed by Emrich and Piedmonte (1991).

...

May include additional arguments that have been deprecated and are no longer used.

References

Emrich LJ, Piedmonte MR. A Method for Generating High-Dimensional Multivariate Binary Variates. The American Statistician 1991;45:302-4.

Examples

# Wide example

def <- defData(varname = "xbase", formula = 5, variance = .4, dist = "gamma", id = "cid")
def <- defData(def, varname = "lambda", formula = ".5 + .1*xbase", dist = "nonrandom", link = "log")

dt <- genData(100, def)

addCorGen(
  dtOld = dt, idvar = "cid", nvars = 3, rho = .7, corstr = "cs",
  dist = "poisson", param1 = "lambda"
)

# Long example

def <- defData(varname = "xbase", formula = 5, variance = .4, dist = "gamma", id = "cid")
def <- defData(def, "nperiods", formula = 3, dist = "noZeroPoisson")

def2 <- defDataAdd(
  varname = "p", formula = "-3+.2*period + .3*xbase",
  dist = "nonrandom", link = "logit"
)

dt <- genData(100, def)

dtLong <- addPeriods(dt, idvars = "cid", nPeriods = 3)
dtLong <- addColumns(def2, dtLong)